{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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b4xdW1LcXbNzJok2ps23mb9jJPaNnA3DVed256ZGpAJxXfApf/WMZAH/48j/xv0a+B8Bj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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff15568ad90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import sys\n",
    "#sys.path.insert(0,'/home/csunix/schtmt/NewFolder/caffe_May2017/python')\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import caffe\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "pp = PdfPages('multipage.pdf')\n",
    "# data = np.load('train_loss.npy')\n",
    "# np.shape(data['train_loss'])\n",
    "\n",
    "# data = np.load('train_loss_Sep2017.npy')\n",
    "# print data.shape\n",
    "# data = data[0:60000]\n",
    "# # print data[99]\n",
    "# # print data[199]\n",
    "# # print data[299]\n",
    "# # #print data[64999]\n",
    "# # print np.shape(data)\n",
    "# # print data.min()\n",
    "# plt.figure()\n",
    "# plt.clf()\n",
    "# plt.plot(np.arange(60000),data)\n",
    "# plt.show()\n",
    "\n",
    "# data = np.load('train_cross_entropy_loss_Sep2017.npy')\n",
    "data = np.load('train_loss_leakyReLU_Sep2017.npy')\n",
    "\n",
    "print data.shape\n",
    "data = data[0:100000]\n",
    "plt.figure()\n",
    "plt.clf()\n",
    "plt.plot(np.arange(100000),data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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